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Cancer is a leading cause of human death worldwide, and the modulation of the metabolic properties of T cells employed in cancer immunotherapy holds great promise for combating cancer. As a crucial factor, energy metabolism influences the activation, proliferation, and function of T cells, and thus metabolic reprogramming of T cells is a unique research perspective in cancer immunology. Special conditions within the tumor microenvironment and high-energy demands lead to alterations in the energy metabolism of T cells. In-depth research on the reprogramming of energy metabolism in T cells can reveal the mechanisms underlying tumor immune tolerance and provide important clues for the development of new tumor immunotherapy strategies as well. Therefore, the study of T cell energy metabolism has important clinical significance and potential applications. In the study, the current achievements in the reprogramming of T cell energy metabolism were reviewed. Then, the influencing factors associated with T cell energy metabolism were introduced. In addition, T cell energy metabolism in cancer immunotherapy was summarized, which highlighted its potential significance in enhancing T cell function and therapeutic outcomes. In summary, energy exhaustion of T cells leads to functional exhaustion, thus resulting in immune evasion by cancer cells. A better understanding of reprogramming of T cell energy metabolism may enable immunotherapy to combat cancer and holds promise for optimizing and enhancing existing therapeutic approaches.

Antithrombin (AT) plays a crucial role in the human anticoagulant system and has extensive clinical applications. However, traditional detection methods often require large sample volumes, complex procedures, and lengthy processing times. Methods: We integrated digital microfluidics technology with AT detection to develop a point-of-care testing (POCT) device that is user-friendly and fully automated for real-time AT testing. Results: This device allows for automation and enhanced adaptability to various settings, requiring only a minimal sample volume (whole capillary blood), thereby omitting steps such as plasma separation to save time and improve clinical testing efficiency. Comparisons with conventional AT activity detection methods demonstrate a high degree of consistency in the results obtained with this device. Conclusion: The AT detection system we developed exhibits significant effectiveness and holds substantial research potential, positioning it to evolve into a clinically impactful POCT solution for AT assessment.

Background and objective Morphological identification of peripheral leukocytes is a complex and time-consuming task, having especially high requirements for personnel expertise. This study is to investigate the role of artificial intelligence (AI) in assisting the manual leukocyte differentiation of peripheral blood. Methods A total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded. Results For junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI. Conclusion AI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.

This narrative review summarizes research progress on plant-derived extracellular vesicles (PEVs) for nanomedicine in cardiopulmonary system diseases, based on key literature covering isolation, engineering, and disease mechanisms. PEVs possess high biocompatibility, low immunogenicity, broad source availability, and scalability. Their bioactive cargo (proteins, nucleic acids, lipids, secondary metabolites) regulates inflammation, oxidative stress, apoptosis, and fibrosis. This review systematically discusses PEV characteristics, large-scale isolation, and engineering approaches, with a focus on multi-target and cell-specific mechanisms in atherosclerosis, myocardial infarction, COPD, and pulmonary fibrosis. Although challenges in standardization, in vivo mechanisms, and translation remain, engineered PEVs hold promise as efficient and safe nanomedicines. The unique contribution of this review is to integrate PEV preparation and engineering with their disease-specific mechanisms, providing a coherent framework for future translational research in cardiopulmonary nanomedicine.

Background This study aims to preliminarily explore the advantages and potential issues of artificial intelligence in the teaching of blood cell morphology to undergraduate medical students, so as to provide theoretical support and practical experience for promoting the intelligent transformation of medical education. Methods Undergraduate students from the 2021 cohort of the Aerospace School of Clinical Medicine at Peking University were assigned as the experimental group, while students from the 2020 cohort served as the control group. The experimental group utilized the AI platform to study blood cell morphology, whereas the control group relied on conventional teaching methods. We compared the accuracy rates of cell identification between two groups of students. Additionally, we conducted supplementary research through questionnaires, post-class interviews, and classroom observations. Results The experimental group achieved a significantly higher average score in cell identification (87.82 ± 9.63) compared to the control group (74.83 ± 12.41) ( P <0.0001). The correct identification rates of metamyelocytes, eosinophils, and monocytes in the experimental group were significantly increased by over 30%. Discussion AI holds considerable promise in medical education, particularly in the instruction of hematology cell morphology. Nevertheless, further research is required. Traditional microscope-based teaching should not be completely dismissed, as current digital platforms for blood cells do not yet capture all cellular nuances.

Objective To investigate the associations between longitudinal body roundness index (BRI) trajectories and the risk of incident diabetes mellitus (DM) using data from the China Health and Retirement Longitudinal Study (CHARLS). Methods Group-based trajectory modeling (GBTM) identified distinct BRI trajectories (Waves 1–3, 2011–2016). Their associations with DM incidence (Wave 4, 2017–2018) were assessed using multivariate Cox models. The predictive performance of a single baseline BRI was compared with body mass index (BMI) and waist circumference (WC) via receiver operating characteristic ( ROC ) analysis. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) evaluated the incremental value of adding BRI trajectories to a conventional risk model. Subgroup and sensitivity analyses, including a landmark approach, assessed robustness. Results Among 4,150 participants, 103 developed DM. Three stable BRI trajectories were identified: low-stable (49.0%), moderate-stable (41.3%), and high-stable (9.7%). Compared with the low-stable group, the high-stable group had a significantly increased DM risk with a fully-adjusted hazard ratio ( HR ) of 2.63 (95% confidence interval [ CI ]: 1.41–4.91). A single baseline BRI showed comparable discrimination to BMI and WC (AUC ≈ 0.63). Longitudinal trajectories of BRI, BMI, and WC all identified high-stable subgroups with elevated risk ( HR s: BRI = 2.63, BMI = 2.16, WC = 2.31), with overlapping confidence intervals. However, adding BRI trajectories to a conventional model significantly improved risk reclassification (NRI = 10.76%, 95% CI : 2.40–19.47) and discrimination (IDI = 0.27%, 95% CI : 0.03–0.52). Results were consistent across subgroups and sensitivity analyses. Conclusions Sustained high BRI exposure, captured by longitudinal trajectory modeling, is independently associated with increased DM risk. While BRI trajectories were not statistically superior to BMI or WC trajectories, the longitudinal framework itself adds value over single-time-point assessments by more robustly identifying individuals with persistent high adiposity-related risk, highlighting the utility of monitoring long-term body shape stability for early risk stratification.

Visceral leishmaniasis (VL) is a life-threatening parasitic infection transmitted by sand flies. Its clinical manifestations can overlap with those of autoimmune diseases, such as systemic lupus erythematosus (SLE). Traditional diagnostic approaches, like bone marrow aspiration, have limited sensitivity for detecting the parasite in peripheral blood. Immunosuppressed patients often present atypically, further complicating diagnosis. Rarely, amastigotes (Leishman-Donovan bodies) can be found within neutrophils, which challenges the conventional view that the parasite primarily infects monocytes/macrophages. A 73-year-old male patient with a history of SLE was on immunosuppressive therapy. The patient presented with persistent pancytopenia and splenomegaly. The prior SLE diagnosis contributed to a delay in recognizing VL. A meticulous examination of the peripheral blood smear first revealed Leishman-Donovan bodies within neutrophils, providing the critical diagnostic clue. This finding prompted further investigations, and the diagnosis was subsequently confirmed by the combination of bone marrow aspiration and serological testing. His immunosuppressed state likely masked typical inflammatory responses and increased his vulnerability to this opportunistic infection, highlighting the difficulty in distinguishing between autoimmune and parasitic diseases. This case underscores the diagnostic challenge of VL in immunosuppressed patients. Symptom overlap with autoimmune disorders and atypical parasite locations (e.g., within neutrophils) can delay diagnosis. Morphological examination of blood and bone marrow remains crucial for detecting rare pathogens. In endemic areas, clinicians should routinely check for parasites within neutrophils to reduce misdiagnosis. Future studies should explore neutrophil-parasite interactions and improve infection monitoring strategies in immunocompromised hosts.

Blockchain systems face significant scalability challenges due to growing data volumes and increasing transaction demands, necessitating more efficient data structures and verification mechanisms. Verkle trees, a novel data structure combining the efficiency of Merkle trees with the compactness of vector commitments, have gained attention for their potential to optimize blockchain storage and improve scalability. However, their practical implementation, especially at the smart contract level, has remained unexplored. To address these challenges, we present TS-verkle, the first known TypeScript-native implementation of Verkle trees designed for web3 backend compatibility, coupled with a corresponding on-chain verifier written in Solidity. Our work bridges this gap by providing a concrete implementation of Verkle trees and demonstrating their feasibility for on-chain verification. While previous literature suggests Verkle trees should outperform Merkle trees due to their succinct proof size, our empirical evaluation reveals that basic implementations of Verkle trees actually incur higher costs than Merkle trees without advanced optimization techniques. This finding represents a crucial insight for blockchain developers and researchers considering Verkle tree adoption. The paper discusses implementation strategies and performance characteristics while exploring implications for scaling and data availability in decentralized blockchain systems.

To meet the vibration control needs working in varied vibration conditions, a new type of semi-active controllable friction was developed in this study, called Self-feedback-friction Damper, with the control of the feedback signal of vibration amplitude. Through calculation and analysis of its work capacity, the parameters of the Self-feedback-friction Damper are determined. The research shows the damping force of new friction damper changes to adjust the varied displacement, to obtain a better damping effect, controlled by the signals of the vibration amplitude.

Connected autonomous vehicles (CAVs), which represent a significant advancement in autonomous driving technology, have the potential to greatly increase traffic safety and efficiency through cooperative decision-making. However, existing methods often overlook the individual needs and heterogeneity of cooperative participants, making it difficult to transfer them to environments where they coexist with human-driven vehicles (HDVs).To address this challenge, this paper proposes an adaptive potential game (APG) cooperative driving framework. First, the system utility function is established on the basis of a general form of individual utility and its monotonic relationship, allowing for the simultaneous optimization of both individual and system objectives. Second, the Shapley value is introduced to compute each vehicle's marginal utility within the system, allowing its varying impact to be quantified. Finally, the HDV preference estimation is dynamically refined by continuously comparing the observed HDV behavior with the APG's estimated actions, leading to improvements in overall system safety and efficiency. Ablation studies demonstrate that adaptively updating Shapley values and HDV preference estimation significantly improve cooperation success rates in mixed traffic. Comparative experiments further highlight the APG's advantages in terms of safety and efficiency over other cooperative methods. Moreover, the applicability of the approach to real-world scenarios was validated through field tests.